AI Software Engineer

Position Summary / Project Description:

The Full Stack Application Engineer will develop and maintain data-intensive applications that powers GenAI solutions, transforming healthcare data into actionable insights at a national level.

In this role, the candidate will collaborate with AI engineers, data scientists, and business stakeholders to build scalable, cloud-native applications, with a strong focus on GenAI integration and Data Analytics.

Roles And Responsibilities:

• Develop, test, deploy, and maintain full stack applications supporting analytics and GenAI use cases.
• Build APIs and interfaces to integrate AI/GenAI models into healthcare systems.
• Collaborate with data engineering teams to ensure robust data pipelines, optimized for AI workloads.
• Implement best practices in cloud-native development, CI/CD, security, and DevOps to ensure high-quality, scalable solutions.
• Monitor application health and basic observability metrics (logs, metrics, traces); escalate anomalies and support root-cause analysis.
• Maintain clear technical documentation (runbooks, design notes, API docs) and contribute to knowledge-sharing sessions.
• Work closely with AI/GenAI teams to operationalize models and deliver AI-powered features.
• Contribute to service monitoring, incident management, and reporting.

Requirements / Qualifications:

• 5+ years of experience in full stack application development with strong hands-on exposure to both frontend and backend.
• Cloud Service Provider (CSP) experience: Proficiency in AWS and Azure platforms, including native cloud services and databases (e.g., DynamoDB, Cosmos DB, RDS, etc.).
• Strong backend experience in Java (Spring Boot) and Python for building APIs and microservices.
• Proficiency in frontend development with frameworks such as React (preferred), Angular, or Vue.js, along with TypeScript, JavaScript, HTML5, and CSS.
• Experience with relational databases and SQL; exposure to NoSQL (e.g., MongoDB) or data warehouses is useful.

Desirable Skills:

• Experience operationalizing AI/ML/GenAI models in production.
• Familiarity with AI/ML services on AWS (e.g., Bedrock, SageMaker) or Azure (e.g., Azure ML, OpenAI Service).
• Prior experience with national-scale, public sector, or healthcare projects is an advantage.

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